Inhalt

[ 404MMMCWFAV23 ] VL Wavelets – Functional Analytical Basics

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M - Master's programme Mathematics Ronny Ramlau 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computational Mathematics 2025W
Learning Outcomes
Competences
The students extract from Fourier- or wavelet transform (or Fourier- and Wavelet series decompositions) of a time series information from the time-frequency behavior of the function. Using the discrete Wavelet Transform they can compress data.
Skills Knowledge
  • Compute the Fourier/ windowed Fourier/Wavelet Transform of a function
  • decompose and reconstruct a function with respect to a frame
  • decompose/reconstruct a function with respect to a orthogonal wavelet basis
  • Signal- and image compression using the discrete wavelet transform
none
Criteria for evaluation Oral exam
Methods Blackbord presentation
Language English
Changing subject? No
Further information
  • Lecture notes;
  • Ten Lectures on Wavelets by Ingrid Daubechies;
  • Wavelets by Louis, Maass, Rieder.
Earlier variants They also cover the requirements of the curriculum (from - to)
403MMIEWFAV22: VL Wavelets – Functional Analytical Basics (2022W-2023S)
On-site course
Maximum number of participants -
Assignment procedure Direct assignment